---
title: Google Generative AI Embeddings (AI Studio & Gemini API)
sidebarTitle: GoogleGenerativeAIEmbeddings
---

Connect to Google's generative AI embeddings service using the `GoogleGenerativeAIEmbeddings` class, found in the [langchain-google-genai](https://pypi.org/project/langchain-google-genai/) package.

This will help you get started with Google's Generative AI embedding models (like Gemini) using LangChain. For detailed documentation on `GoogleGenerativeAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/google_genai/embeddings/langchain_google_genai.embeddings.GoogleGenerativeAIEmbeddings.html).

## Overview

### Integration details

<ItemTable category="text_embedding" item="Google Gemini" />

## Setup

To access Google Generative AI embedding models you'll need to create a Google Cloud project, enable the Generative Language API, get an API key, and install the `langchain-google-genai` integration package.

### Credentials

To use Google Generative AI models, you must have an API key. You can create one in Google AI Studio. See the [Google documentation](https://ai.google.dev/gemini-api/docs/api-key) for instructions.

Once you have a key, set it as an environment variable `GOOGLE_API_KEY`:

```python
import getpass
import os

if not os.getenv("GOOGLE_API_KEY"):
    os.environ["GOOGLE_API_KEY"] = getpass.getpass("Enter your Google API key: ")
```

To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:

```python
# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
```

## Installation

```python
%pip install -qU  langchain-google-genai
```

## Usage

```python
from langchain_google_genai import GoogleGenerativeAIEmbeddings

embeddings = GoogleGenerativeAIEmbeddings(model="models/gemini-embedding-001")
vector = embeddings.embed_query("hello, world!")
vector[:5]
```

```output
[-0.024917153641581535,
 0.012005362659692764,
 -0.003886754624545574,
 -0.05774897709488869,
 0.0020742062479257584]
```

## Batch

You can also embed multiple strings at once for a processing speedup:

```python
vectors = embeddings.embed_documents(
    [
        "Today is Monday",
        "Today is Tuesday",
        "Today is April Fools day",
    ]
)
len(vectors), len(vectors[0])
```

```output
(3, 3072)
```

## Indexing and Retrieval

Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/oss/langchain/rag).

Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`.

```python
# Create a vector store with a sample text
from langchain_core.vectorstores import InMemoryVectorStore

text = "LangChain is the framework for building context-aware reasoning applications"

vectorstore = InMemoryVectorStore.from_texts(
    [text],
    embedding=embeddings,
)

# Use the vectorstore as a retriever
retriever = vectorstore.as_retriever()

# Retrieve the most similar text
retrieved_documents = retriever.invoke("What is LangChain?")

# show the retrieved document's content
retrieved_documents[0].page_content
```

```output
'LangChain is the framework for building context-aware reasoning applications'
```

## Task type

`GoogleGenerativeAIEmbeddings` optionally support a `task_type`, which currently must be one of:

- `SEMANTIC_SIMILARITY`: Used to generate embeddings that are optimized to assess text similarity.
- `CLASSIFICATION`: Used to generate embeddings that are optimized to classify texts according to preset labels.
- `CLUSTERING`: Used to generate embeddings that are optimized to cluster texts based on their similarities.
- `RETRIEVAL_DOCUMENT`, `RETRIEVAL_QUERY`, `QUESTION_ANSWERING`, and `FACT_VERIFICATION`: Used to generate embeddings that are optimized for document search or information retrieval.
- `CODE_RETRIEVAL_QUERY`: Used to retrieve a code block based on a natural language query, such as sort an array or reverse a linked list. Embeddings of the code blocks are computed using `RETRIEVAL_DOCUMENT`.

By default, we use `RETRIEVAL_DOCUMENT` in the `embed_documents` method and `RETRIEVAL_QUERY` in the `embed_query` method. If you provide a task type, we will use that for all methods.

```python
%pip install -qU  matplotlib scikit-learn
```

```python
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from sklearn.metrics.pairwise import cosine_similarity

query_embeddings = GoogleGenerativeAIEmbeddings(
    model="models/gemini-embedding-001", task_type="RETRIEVAL_QUERY"
)
doc_embeddings = GoogleGenerativeAIEmbeddings(
    model="models/gemini-embedding-001", task_type="RETRIEVAL_DOCUMENT"
)

q_embed = query_embeddings.embed_query("What is the capital of France?")
d_embed = doc_embeddings.embed_documents(
    ["The capital of France is Paris.", "Philipp is likes to eat pizza."]
)

for i, d in enumerate(d_embed):
    print(f"Document {i + 1}:")
    print(f"Cosine similarity with query: {cosine_similarity([q_embed], [d])[0][0]}")
    print("---")
```

```output
Document 1
Cosine similarity with query: 0.7892893360164779
---
Document 2
Cosine similarity with query: 0.5438283285204146
---
```

## API Reference

For detailed documentation on `GoogleGenerativeAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/google_genai/embeddings/langchain_google_genai.embeddings.GoogleGenerativeAIEmbeddings.html).

## Additional Configuration

You can pass the following parameters to ChatGoogleGenerativeAI in order to customize the SDK's behavior:

- `client_options`: [Client Options](https://googleapis.dev/python/google-api-core/latest/client_options.html#module-google.api_core.client_options) to pass to the Google API Client, such as a custom `client_options["api_endpoint"]`
- `transport`: The transport method to use, such as `rest`, `grpc`, or `grpc_asyncio`.
